Artificial intelligence and mathematical theory of computation
A circumscriptive calculus of events
Artificial Intelligence
Proceedings of the 1999 international conference on Logic programming
Grounding symbols through evolutionary language games
Simulating the evolution of language
Relational Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning to Communicate and Act Using Hierarchical Reinforcement Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Interactive POMDPs: Properties and Preliminary Results
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Machine Learning
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Word Sense Disambiguation Using Inductive Logic Programming
Inductive Logic Programming
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
Learning with whom to communicate using relational reinforcement learning
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Circumscriptive event calculus as answer set programming
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Datum-wise classification: a sequential approach to sparsity
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Learning from natural instructions
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Integrating relational reinforcement learning with reasoning about actions and change
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
| Hi-index | 0.00 |
We present a framework for the machine learning of denotational concept semantics using a simple form of symbolic interaction of machines with human users. The capability of software agents and robots to learn how to communicate verbally with human users would obviously be highly useful in several real-world applications, and our framework is meant to provide a further step towards this goal. Whereas the large majority of existing approaches to the machine learning of word sense and other language aspects focuses on learning using text corpora, our framework allows for the interactive learning of concepts in a dialog of human and agent, using an approach in the area of Relational Reinforcement Learning. Such an approach has a wide range of possible applications, e.g., the interactive acquisition of semantic categories for the Semantic Web, Human-Computer Interaction, (interactive) Information Retrieval, and Natural Language Processing.